Al-Ahliyya Amman University is located in Amman, Jordan. Founded in 1990, it was the first private university in Jordan. The university is accredited by the Ministry of Higher Education and Scientific Research, Jordan, and is a member of four university associations. Foreign students come from a diversity of countries, for example Syria, Iraq, the United States, Japan and Israel. Wikipedia.
Musmar M.A.,Al-Ahliyya Amman University
Jordan Journal of Civil Engineering | Year: 2013
The use of shear wall-buildings is quite common in some earthquake prone regions. During seismic excitation, they contribute in absorbing moments and shear forces and reduce torsional response. Usually, architectural design leads to the existence of doors and windows within shear walls. Previous researches on the behavior of shear walls with openings assumed elastic analysis utilizing shell and brick elements. The present work adopts nonlinear finite element analysis using solid65 element. The analysis comprises both material and geometric nonlinearities. Solid65 element models the nonlinear response of concrete material based on a constitutive model for the triaxial behavior of concrete after Williams and Warnke. Five shear wall models with different opening sizes are analyzed. A sixth model of a solid shear wall is also presented to compare the analysis results. The paper studies the effect of the size of the openings on the behavior of the reinforced concrete shear walls. The study indicates that openings of small dimensions yield minor effects on the response of shear walls with respect to both normal stresses along the base level of shear walls and maximum drift. Cantilever behavior similar to that of a solid shear wall takes place and analogous to that of coupled shear walls. On the other hand, when openings are large enough, shear walls behave as connected shear walls, exhibiting frame action behavior. © 2013 JUST. All Rights Reserved.
Hassan M.,Al-Ahliyya Amman University
International Arab Journal of Information Technology | Year: 2013
Current Internet trends are moving towards decentralization of computation, storage, and resources. Supporting network management for such a vast and a highly complex system has become a challenging issue. A management platform has to sufficiently support decentralization, collaboration, and integration. Grid technologies have the potential to serve as management architecture due to the support of the above features. In this paper, we developed a collaborative network management architecture leveraging the key features of grid technology. Benefiting from this integration, we were able to show that multiple management tasks can be integrated and completed in parallel. This assures the management scalability and efficiency. We also showed that the management information at different networking domains can freely consume the computational resources provided through the grid interface while being executed. Grid interface has guaranteed scalability and reliability for the network management tasks. We have simulated the system prototype and closely studied its efficiency.
AL-Tahrawi M.,Al-Ahliyya Amman University
UPB Scientific Bulletin, Series C: Electrical Engineering | Year: 2015
Feature Selection (FS) is a crucial preprocessing step in Text Classification (TC) systems. FS can be either Class-Based or Corpus-Based. Polynomial Network (PN) classifiers have proved recently to be competitive in TC using a very small subset of corpora features. This paper presents an empirical study of the performance of PN classifiers using Aggressive Class-Based FS. Seven of the stateof- the art FS metrics are experimented and compared: Chi Square (CHI), Information Gain (IG), Odds Ratio (OR), GSS, NGL coefficient, Document Frequency (DF), and Gain Ratio (GR).The study is conducted on the Reuters Benchmark Corpus. Experimental results are presented in terms of both micro-averaged and macro-averaged precision, recall and F measures. Results reveal that aggressive Class-Based Chi-Square and DF metrics work best for Reuters using PN classifiers compared to the other five FS metrics experimented in this research.
Al-Tahrawi M.M.,Al-Ahliyya Amman University
International Journal of Intelligent Systems | Year: 2014
The significance of low frequent terms in text classification (TC) was always debatable. These terms were often accused of adding noise to the TC process. Nevertheless, some recent studies have proved that they are very helpful in improving the performance of text classifiers. This paper shows the significance of low frequent terms in enhancing the performance of English TC, in terms of precision, recall, F-measure, and accuracy. Six well-known TC algorithms are tested on the benchmark Reuters Data Set, once keeping low frequent terms and another time discarding them. These algorithms are the support vector machines, logistic regression, k-nearest neighbor, naive bayes, the radial basis function networks, and polynomial networks. All the experiments in this research have shown a superior performance of TC when the low frequent terms are used in classification. © 2014 Wiley Periodicals, Inc.
Ammari W.G.,Al-Ahliyya Amman University
Tropical Journal of Pharmaceutical Research | Year: 2015
Purpose: The current work validated a high performance liquid chromatography-tandem mass spectrometric (HPLC-MS/MS) bioassay method developed in-house for the quantitation of solifenacin in human plasma. Methods: Solifenacin was extracted from plasma by a liquid-liquid extraction (LLE) technique using tertbutyl methyl ether. The dry extract was then reconstituted with 200 μL of the mobile phase (acetonitrilewater (80:20, v/v)). Solifenacin-d5 was the internal standard (IS). Elution was carried out on a C18 column at a flow rate of 1 mL/min. The MS/MS employed turbo-ion spray ionization in the positive ion mode. Solifenacin and IS were monitored at a mass to charge ratio (m/z) of 363.4 and 368.4, respectively. Bioassay validation followed International Bioanalytical Method Validation Guidelines. Results: The validated calibration curves were linear over a range of 0.5 – 60.0 ng/mL (regression factors ≥ 0.9994). Method specificity was established in 6 different human plasma batches. Intra- and inter-day precision and accuracy were within ± 20% (for lower limit of quantitation (LLOQ)) and ± 15% (for low, mid and high quality control (QC) levels). Short- and long-term stability was within accepted range. Conclusion: A specific, accurate and precise HPLC-MS/MS method has been validated for the determination of solifenacin in human plasma. © Pharmacotherapy Group, Faculty of Pharmacy, University of Benin, Benin City, 300001 Nigeria. All rights reserved.